提出可泛化到不同规模集群的智能调度方法，无需重新训练
SCALE: Scalable Cross-Attention Learning with Extrapolation for Agentic Workflow Scheduling
- 用交叉注意力机制动态分配任务到任意数量服务器
- 在32和48节点上平均响应时间降低8.9%（相比无正则化版本）
- 适合需要跨集群部署的大型语言模型系统研发者
智能大语言模型系统将复杂任务分解为工作流有向无环图，其原语需在异构集群上调度。现有深度强化学习调度器受限于固定集群规模，一旦服务器数量变化就必须重新训练。本文提出SCALE（可扩展交叉注意力学习与外推），一种无需微调即可泛化至未见集群规模的DRL调度器。SCALE采用交叉注意力指针网络，任务特征查询服务器特征，因此架构天然支持任意数量服务器。然而我们发现，仅靠排列不变性不足以保证新规模下的性能——随着服务器数量增加，注意力特征分布发生偏移。为此引入结构化表示正则化（SRR）：结合去相关损失与标准正态分布的KL惩罚，稳定特征统计量。在16节点上训练，在32和48节点上直接测试，相较无SRR的相同架构，SCALE在N=48时平均响应时间降低8.9%，验证了显式正则化对弥合规模泛化差距的必要性。
原文摘要 · Abstract (English)
Agentic Large Language Model (LLM) systems decompose complex tasks into workflow Directed Acyclic Graphs (DAGs) whose primitives must be scheduled on heterogeneous clusters. Existing deep reinforcement learning (DRL) schedulers are tied to a fixed cluster size and require retraining whenever the number of servers changes. We propose SCALE (Scalable Cross-Attention Learning with Extrapolation), a DRL scheduler that generalizes to unseen cluster scales without fine-tuning. SCALE employs a cross-attention pointer network where task features query against server features, so the architecture accepts any number of servers by construction. We observe, however, that permutation-invariant architecture alone does not guarantee good performance at new scales - the attention feature undergoes distribution shift as the server count grows. To counter this, we introduce Structured Representation Regularization (SRR): a decorrelation loss combined with a KL penalty toward the standard normal, which keeps feature statistics stable regardless of input size. Trained on 16 nodes and tested directly on 32 and 48 nodes, SCALE reduces average response time by 8.9% at N=48 relative to the same architecture without SRR, confirming that explicit regularization is necessary to close the scale-generalization gap.
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